Abstract Robust design methods have expanded from experimental techniques to include sampling methods, sensitivity analysis and probabilistic optimisation. Such typically require many evaluations. We study noise variable cross-term second derivatives of a response quickly identify variables that reduce variability. first compute the uncertainty variance decomposition determine contributing an i...

To improve the reliability of simulation results, uncertainty analysis methods were developed in Electromagnetic Compatibility (EMC) field. Random variables are used to describe random events. The more you have, less efficient is. Therefore, many high-accuracy have problem dimensional disaster, which means calculation efficiency decreases exponentially with increase number variables. A variable...

Abstract Smart service innovation is the process of reconfiguring resources, structures, and value co-creation processes in systems that result novel data-driven offerings. The nature such offerings requires involvement multiple actors, which has been investigated by a few studies only. In particular, little known about actors’ efforts to manage uncertainty establishing smart systems. Empirical...

This paper proposes the use of uncertainty reduction in machine learning methods such as co-training and bilingual bootstrapping, which are referred to, in a general term, as ‘collaborative bootstrapping’. The paper indicates that uncertainty reduction is an important factor for enhancing the performance of collaborative bootstrapping. It proposes a new measure for representing the degree of un...